859 research outputs found
AP WEB 2.0 - Proceedings of the International Workshop on Adaptation and Personalization for Web 2.0
No full textThe proceedings contain 19 papers. The topics discussed include: a framework for flexible user profile mashups; handling users local contexts in web 2.0; context-aware notification management in an integrated collaborative environment; a general framework for personalized text classification and annotation; a personalized tag-based recommendation in social web systems; using asynchronous client-side user monitoring to enhance user modeling in adaptive e-learning systems; customized edit interfaces for wikis via semantic annotations; visualizing web server logs for a web 1.0 audience using web 2.0 technologies; new generation of social networks based on semantic web technologies; balanced recommenders: a hybrid approach to improve and extend the functionality of traditional recommenders; visualizing reciprocal and non-reciprocal relationships in an online community; and a user-centric authentication and privacy control mechanism for user model interoperability in social networking sites
Ephemeral and Persistent Personalization in Adaptive Information Access to Scholarly Publications on the Web
No full textExtracting and exploiting topics of interests from social tagging systems
No full textUsers of social tagging systems spontaneously annotate resources providing, in this way, useful information about their interests. A collaborative filtering recommender system can use this feedback in order to identify people and resources more strictly related to a specific topic of interest. Such a collaborative filtering approach can compute similarities among tags in order to select resources associated to tags relevant for a specific interest of the user. Several research works try to infer these similarities by evaluating co-occurrences of tags over the entire set of annotated resources discarding, in this way, information about the personal classification provided by users. This paper, on the other hand, proposes an approach aimed at observing only the set of annotations of a single user in order to identify his topic of interests and to produce personalized recommendations. More specifically, following the idea that each user may have several distinct interests and people may share just some of these interests, our approach adaptively filters and combines the feedback of users according to a specific topic of interest of a user. © 2011 Springer-Verla
User Modeling for Information Filtering on Internet Services: Exploiting an Extended Version of the UMT Shell
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